Embodiments of the present disclosure relate generally to wind turbines, and more particularly to methods and systems for optimizing operation of a wind farm.
Renewable energy resources are increasingly employed as cleaner and cost-efficient alternatives to fossil fuels for supplying global energy requirements. Wind energy, in particular, has emerged as one of the most favored renewable energy resources on account of being plentiful, renewable, widely distributed, and clean. Generally, wind energy may be harnessed by wind turbines that are designed to produce electrical energy in response to a wide spectrum of wind speeds. These wind turbines are typically located in a wind farm spread across a specific geographical region such that the wind passing over the region causes the blades associated with the wind turbines to rotate. Each of the rotating blades, in turn, causes a rotor of an associated generator to turn, which aids in generating electrical power.
Traditionally, wind farms are controlled in a decentralized fashion to generate power such that each turbine is operated to maximize local power output and to minimize impacts of local fatigue and extreme loads. However, in practice, such independent optimization of the wind turbines ignores farm-level performance goals, thereby leading to sub-optimal performance at the wind farm-level. For example, independent optimization of the wind turbines may not account for aerodynamic interactions such as wake effects between neighboring turbines within the wind farm that may affect a farm-level power output.
Typically, wake effects include a reduction in wind speed and increased wind turbulence at a downstream wind turbine due to a conventional operation of an upstream wind turbine. The reduced wind speed causes a proportional reduction in a power output of the downstream wind turbine. Moreover, the increased turbulence increases the fatigue loads placed on the downstream wind turbine. Several studies have reported a loss of more than 10% in the annual energy production (AEP) of the wind farm owing to the wake effects between neighboring independently optimized wind turbines within the wind farm.
Accordingly, some currently available approaches attempt to optimize power generation at the wind farm-level by mitigating an impact of the wake effects through a coordinated control of the wind turbines in the wind farm. Typically, mitigating the wake effects involves accurately modeling the wake effects experienced at different wind turbines in the wind farm. For example, empirical or semi-empirical thrust-based, and/or high fidelity physics-based models may be used to model the wake effects between the aerodynamically interacting wind turbines in the wind farm.
Conventionally, the empirical or semi-empirical models (engineering wake models) are generated based on field-experiment data and/or historical wind information. Accordingly, these models may be used to design the layouts of wind farms so as to optimize one or more performance goals before installation of the wind turbines. Alternatively, these models may be used to optimize performance of the wind farm subsequent to the installation.
One optimization approach, for example, employs the engineering wake models to determine control settings for the wind turbines. Particularly, the engineering wake models determine the control settings so as to operate upstream turbines at lower efficiencies, which in turn, allows for greater energy recovery at the downstream turbines. Another approach uses the engineering wake models for adjusting a yaw alignment of the upstream turbines relative to an incoming wind direction to steer the resulting wake effects away from the downstream turbines.
However, the conventional engineering models do not account for prevailing wind inflow and other ambient conditions such as atmospheric boundary layer stability and longitudinal turbulence intensity. As the ambient conditions over the wind farm tend to change frequently, the wake models estimated using the engineering wake models may be inaccurate for use during real-time implementation. Inaccurate modeling of the wake conditions, in turn, may result in use of incorrect control settings for the wind turbines in the wind farm. Thus, the conventional optimization approaches using the engineering wake models usually provide only a marginal improvement in the farm-level performance output.
Accordingly, hi-fidelity wake models, for example, based on computational fluid dynamics modeling have been explored to provide greater accuracy in modeling wake interactions. The hi-fidelity models entail measurement and analysis of a wide variety of parameters that necessitate additional instrumentation, complex computations, and associated costs. The cost and complexity associated with the hi-fidelity models, therefore, may preclude wider use of these models in every turbine in the wind farm and/or for real time optimization of wind farm operations.